AI RESEARCH
AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction
arXiv CS.CV
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ArXi:2603.05551v1 Announce Type: cross Information-intensive Document Question Answering (DocQA) is often constrained by long contexts and information overload, which hinders Vision-Language Models (VLMs) from performing precise direct reasoning. Although multimodal GraphRAG has achieved preliminary breakthroughs, existing frameworks still face dual challenges: (1) the necessity of large-scale models for handling queries of diverse complexities and (2) the inherent reasoning bottlenecks of end-to-end VLMs.